{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "amino-walter",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'list' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-724afd27b555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;31m# from tushareapi import tusharekline as klineapi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# import tushare as ts #需要积分才能获取\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
     ]
    }
   ],
   "source": [
    "import  pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "\n",
    "#import warnings\n",
    "\n",
    "#warnings.filters('ignore')\n",
    "# from tushareapi import tusharekline as klineapi\n",
    "# import tushare as ts #需要积分才能获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "committed-offense",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Argument prefix must be None if argument header is not None",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-29ef5f76a17a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_hsi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'datasets/hsi.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hsi'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_hscei\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'datasets/hscei.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hscei'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1985\u001b[0m         \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1987\u001b[0;31m         \u001b[0mParserBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1989\u001b[0m         \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, kwds)\u001b[0m\n\u001b[1;32m   1479\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprefix\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m                 raise ValueError(\n\u001b[0;32m-> 1481\u001b[0;31m                     \u001b[0;34m\"Argument prefix must be None if argument header is not None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1482\u001b[0m                 )\n\u001b[1;32m   1483\u001b[0m             \u001b[0;31m# GH 16338\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Argument prefix must be None if argument header is not None"
     ]
    }
   ],
   "source": [
    "df_hsi = pd.read_csv('datasets/hsi.csv', prefix='H')\n",
    "df_hscei = pd.read_csv('datasets/hscei.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "loose-charger",
   "metadata": {},
   "outputs": [],
   "source": [
    "hsiRet = df_hsi.ret\n",
    "hsceiRet = df_hscei.ret\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "assumed-stationery",
   "metadata": {},
   "source": [
    "dfall=pd.merge(df_hsi, df_hscei, how='inner', on=None, left_on=None, right_on=None,\n",
    "         left_index=False, right_index=False, sort=True,\n",
    "         suffixes=('_x', '_y'), copy=True, indicator=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "banner-diversity",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfall = pd.merge(df_hsi, df_hscei,on='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cathedral-stand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>symbol_x</th>\n",
       "      <th>open_x</th>\n",
       "      <th>high_x</th>\n",
       "      <th>low_x</th>\n",
       "      <th>close_x</th>\n",
       "      <th>preclose_x</th>\n",
       "      <th>vol_x</th>\n",
       "      <th>value_x</th>\n",
       "      <th>ret_x</th>\n",
       "      <th>symbol_y</th>\n",
       "      <th>open_y</th>\n",
       "      <th>high_y</th>\n",
       "      <th>low_y</th>\n",
       "      <th>close_y</th>\n",
       "      <th>preclose_y</th>\n",
       "      <th>vol_y</th>\n",
       "      <th>value_y</th>\n",
       "      <th>ret_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04T00:00:00Z</td>\n",
       "      <td>HSI</td>\n",
       "      <td>21860.04</td>\n",
       "      <td>22024.83</td>\n",
       "      <td>21689.22</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>21872.50</td>\n",
       "      <td>48509170000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002250</td>\n",
       "      <td>HSCEI</td>\n",
       "      <td>12791.33</td>\n",
       "      <td>12928.77</td>\n",
       "      <td>12642.10</td>\n",
       "      <td>12750.55</td>\n",
       "      <td>12794.13</td>\n",
       "      <td>10530514278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05T00:00:00Z</td>\n",
       "      <td>HSI</td>\n",
       "      <td>22092.15</td>\n",
       "      <td>22297.04</td>\n",
       "      <td>21987.27</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>82973500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>HSCEI</td>\n",
       "      <td>12910.37</td>\n",
       "      <td>13157.77</td>\n",
       "      <td>12868.65</td>\n",
       "      <td>13142.03</td>\n",
       "      <td>12750.55</td>\n",
       "      <td>23806618223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06T00:00:00Z</td>\n",
       "      <td>HSI</td>\n",
       "      <td>22357.46</td>\n",
       "      <td>22514.79</td>\n",
       "      <td>22277.13</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>91328340000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>HSCEI</td>\n",
       "      <td>13175.11</td>\n",
       "      <td>13350.12</td>\n",
       "      <td>13114.74</td>\n",
       "      <td>13246.21</td>\n",
       "      <td>13142.03</td>\n",
       "      <td>27154897598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07T00:00:00Z</td>\n",
       "      <td>HSI</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22169.61</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>79167640000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.006567</td>\n",
       "      <td>HSCEI</td>\n",
       "      <td>13354.97</td>\n",
       "      <td>13354.97</td>\n",
       "      <td>13019.03</td>\n",
       "      <td>13073.20</td>\n",
       "      <td>13246.21</td>\n",
       "      <td>20423343202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.013061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08T00:00:00Z</td>\n",
       "      <td>HSI</td>\n",
       "      <td>22282.75</td>\n",
       "      <td>22443.22</td>\n",
       "      <td>22206.16</td>\n",
       "      <td>22296.75</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>71931720000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>HSCEI</td>\n",
       "      <td>13082.70</td>\n",
       "      <td>13131.05</td>\n",
       "      <td>12952.18</td>\n",
       "      <td>13035.09</td>\n",
       "      <td>13073.20</td>\n",
       "      <td>18411144930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time symbol_x    open_x    high_x     low_x   close_x  \\\n",
       "0  2010-01-04T00:00:00Z      HSI  21860.04  22024.83  21689.22  21823.28   \n",
       "1  2010-01-05T00:00:00Z      HSI  22092.15  22297.04  21987.27  22279.58   \n",
       "2  2010-01-06T00:00:00Z      HSI  22357.46  22514.79  22277.13  22416.67   \n",
       "3  2010-01-07T00:00:00Z      HSI  22548.03  22548.03  22169.61  22269.45   \n",
       "4  2010-01-08T00:00:00Z      HSI  22282.75  22443.22  22206.16  22296.75   \n",
       "\n",
       "   preclose_x        vol_x  value_x     ret_x symbol_y    open_y    high_y  \\\n",
       "0    21872.50  48509170000      0.0 -0.002250    HSCEI  12791.33  12928.77   \n",
       "1    21823.28  82973500000      0.0  0.020909    HSCEI  12910.37  13157.77   \n",
       "2    22279.58  91328340000      0.0  0.006153    HSCEI  13175.11  13350.12   \n",
       "3    22416.67  79167640000      0.0 -0.006567    HSCEI  13354.97  13354.97   \n",
       "4    22269.45  71931720000      0.0  0.001226    HSCEI  13082.70  13131.05   \n",
       "\n",
       "      low_y   close_y  preclose_y        vol_y  value_y     ret_y  \n",
       "0  12642.10  12750.55    12794.13  10530514278      0.0 -0.003406  \n",
       "1  12868.65  13142.03    12750.55  23806618223      0.0  0.030703  \n",
       "2  13114.74  13246.21    13142.03  27154897598      0.0  0.007927  \n",
       "3  13019.03  13073.20    13246.21  20423343202      0.0 -0.013061  \n",
       "4  12952.18  13035.09    13073.20  18411144930      0.0 -0.002915  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "funded-council",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.OLS(dfall.ret_x, sm.add_constant(dfall.ret_y)).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "familiar-purple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>ret_x</td>      <th>  R-squared:         </th>  <td>   0.889</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.889</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.236e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 25 Apr 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:05:50</td>     <th>  Log-Likelihood:    </th>  <td>  11472.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2781</td>      <th>  AIC:               </th> <td>-2.294e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2779</td>      <th>  BIC:               </th> <td>-2.293e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0001</td> <td> 7.42e-05</td> <td>    1.859</td> <td> 0.063</td> <td>-7.54e-06</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ret_y</th> <td>    0.7750</td> <td>    0.005</td> <td>  149.545</td> <td> 0.000</td> <td>    0.765</td> <td>    0.785</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>115.489</td> <th>  Durbin-Watson:     </th> <td>   1.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 359.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.060</td>  <th>  Prob(JB):          </th> <td>1.00e-78</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.757</td>  <th>  Cond. No.          </th> <td>    69.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  ret_x   R-squared:                       0.889\n",
       "Model:                            OLS   Adj. R-squared:                  0.889\n",
       "Method:                 Least Squares   F-statistic:                 2.236e+04\n",
       "Date:                Sun, 25 Apr 2021   Prob (F-statistic):               0.00\n",
       "Time:                        19:05:50   Log-Likelihood:                 11472.\n",
       "No. Observations:                2781   AIC:                        -2.294e+04\n",
       "Df Residuals:                    2779   BIC:                        -2.293e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0001   7.42e-05      1.859      0.063   -7.54e-06       0.000\n",
       "ret_y          0.7750      0.005    149.545      0.000       0.765       0.785\n",
       "==============================================================================\n",
       "Omnibus:                      115.489   Durbin-Watson:                   1.895\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              359.203\n",
       "Skew:                          -0.060   Prob(JB):                     1.00e-78\n",
       "Kurtosis:                       4.757   Cond. No.                         69.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "amended-ability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.002502\n",
       "1    0.023932\n",
       "2    0.006281\n",
       "3   -0.009984\n",
       "4   -0.002121\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fittedvalues[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "tender-rental",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '残差 ')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(model.fittedvalues, model.resid)\n",
    "plt.xlabel('拟合值')\n",
    "plt.ylabel('残差 ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "seeing-galaxy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "sm.qqplot(model.resid_pearson, stats.norm,line='45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "internal-toddler",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa1cc01ecc0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/3ER0eNvamotEwfKCgddnKhg9dNpXnLdujLJxmX5E0AUtfhNMgiSk6P5m1qVaoRdrMiyL05ycHPQcw3tP1PzzLolCdqwJP3ZRNlc7Vn+/yQLXQhb9Ju+MT5Yxq9hk1W1cWhOqVGMUkoUIuqDFb0xxkBjkoKLrxZoM2+JcYGhFvbrADffJI3dsaDTCcGOeWSnKD4yfxI6xKc/+eC+rFnM1ZM+wLRYMpQ8+7KxPIXpE0FNAXFaS36V5kKV8ENH1Ggbn11r2gtMGpTk5DQ2UsOASu64S/Ep1Hp/6xgms3nkQTyrqlbvhNjk6uaBGD51uub/aKQEgxIMIesKJ00ryWkfDnHBUbgk38dW9hhM6a9I+6ZnWslmzxJvN7IyT4W2dnHQTValYwEt7btEKvp/kIDvLC7ViZToDQCf4M5Wq8v7SHW/dVxBrPVmIoCecOK0kuyCWioUWMbVOOEDNgjU1T3W8CrMeCVCLcHFyWVgrFarGYBeloYESjuy8GY9u3+Tb4lVR6NF/ZcwiWbrJjbCY9h/FBuRbl+fwwPhJ7bXw+prmSsGpmqW4YJKJhC0mnLjrQbvV0VBNOIzFmGZT4FRhjKr480v1zVCnnp5rdh7E8oIBImBmtqosK2sPCQxjAlzRa2DGpcKj9f3Y3wEDGPvOqzj4vbO4MFv11NXID9V5VtadMa+FrjuSinlmvHlpDvkcYX5BP0ppNpEsxEJPOEkPMXOacNzcRU6rj2LB2TqcqVRxYbYW5aETf+vYwmiEcaE+eahw6jVqpbrAjU3JKFL6nK6FasW1wsEKry6wo5hbzy0kAxH0hJP0etBOE46bu8jJR3vxkr9a5ypyRI3JIxeGAx16wdy6ri9yYfMaOaPC/JxMF9RLe27BkZ0348Hb1re9cZwU4yJs0hiyKYKecLz4sePEacJxcxfphIBQy4Jsl3nmxorAg6HZoFQs4LHtm/Dynluaapc7cfB7Z7XWe1jMM8MIMDNZffd2/IZZqpi9PJcKsfNDWkM2xYeeApJcD9qpMp+u4qAp5LpWaGG6IirVeYwcUHZEVPKyreO9V6vbb/ekIOSpVmvG7zViAPuOlTF4/UrlhvLoodOYZ//nNVE1tE47aW1OLYIutI1uwtGVUt26rq+xUVrsNbC0J9cofxtF02enWuJW7Nb4+GQ5/BnGI4atUBiw6O4JMhxVh6jdz0w3TURmhJL1/6DnTztxByMERVwuQmSo3EVmKVprX9CLlSru3dyPIztv9uziiAKrW8JccsfVqmXZ0kVbKyxPjilGZhaqalXBqK0EgrztpIudH5IejKBDBF2IFPsm3OFT55Rhjl87egbjk2VsXdcXSgJQEKydhEYOTHsu5+sUkRMU66oirEnFbDhtr/luxym5yWnCtW5Cp52kByPoEJdLishC4wCdFccAdj8zjUvVhRaxWZInXFYUqAqb8kwFm3Z/E7duvMaTm6ZYMBqx9n76euYAOJceax+7y8QUo5ED04E9SNbcAtX7NTehgfT70tPanFoEPSWoansn+cvjt80boN9Y7LvqikY3nih87FZmKtWmBtVOXKyLvnn9dfXS7UQt5kCzH9xsCgJ430+wY7VOzff7qW+ccE3oSjNJDkbQIS6XlJCmQklOIV/D29b6dqmYSTFx+9jtLLe4WoYGSm2F/kWBNWPXjDoKyhVGDhOvnG/EZZuRMSqy5EtPG2Khp4Q07bo7TT5Hdt6s7d2pa2hs3YhK0vslal6JxLR/6ohZSKvdCKILs80rF7NWjeo9J33jMMuIhZ4S0rTr7jb5PDy0AY9u39SSLDVye2vWon0jKknv98JsFcN7TzRWIknFS4OOoOe1n9PIE956ey5V2ZVZQgQ9JcS16x4k/dnL5DM0UMLwtrW4tlhotFED0BTmWCwYuMLIYcfYVOO1V78jOYIOtPYhTTJRjNR06xBqxcsQsJOSEA7EMQXaDg4O8sTERCyvnVY6HeWii2ZY0WvgwdvWO3aZVyUU3XljCYdPnWskFL15aa5JEK2beFvX9WHfsXLTOVTJNoJ/wsyVMn30ABplg52OEdqHiI4x86DyORF0QYfuCwrUBFrXaMKMRsnXy9qa//sRkpgSNDNPnghf+LWNLROukSNceUUPZmZrGbuqCVVFsWBg5Pba5L5m50HtZ1ZKSdhfGnASdNkUFbQ4bUCqwtPslrkZBREkZV3EPBrmmT3HWA9ev7LpmK3r+hq13E1mKot1XJw2XpMeZpsVRNAFLW6REXbB91IPXGiPgpEDQIGvsxn26SXGWnXM4VPnWvIFvDbQyFKMelKRTdEMEXb9Zrcmy/bNzySFFGaRHIBH7ngf7rwxWMy7kSflJrqf+8Ypgslau8fv3wvhIBZ6Rogik9T8u5ED0y3x4aoIm3Zine0+c/Ght7IAYO/EGRw/czFQM+llS3oan+kD4yeV7erc7hvdZ3ytzfLX7b8kKew0i4iFnhGiyiQdGihh6sEP4TFF3Lj9C+9m0esoFQstcen3bu5vu5NOFjny4vnA7hazVMED4yfx5NEz2knB6b7xGj6b1uJWaUcs9IwQdSapV58rAN81V8zlumpTbsc3pmIrYRs1BKDYa3SkOQawaB0/9fyrrseWZypYvfMggOYwVa8bqmktbpV2RNAzgttS2AthxLm7LblVMGohkvbXGxooKd09WYFRK41r5AnViKtJWq1jv+6aC7NVDD99AhOvnG/kEVxbX1U53R9pLG6Vdlzj0InoywBuBfATZn6v4nkC8IcAPgJgFsBvMfNxtxeWOPRw0SXz6PqP2sVbFXfs9PdexrNjbCqQH7xYMEAEzMxWu8KPHlU5XXu1RfNzXLPrYKBVj6okb5L623YL7cah/wWAxwF8VfP8hwG8u/7vJgB/Vv9f6CB+lriqDVRV0wOvYWbWDbY8Ee65aRUeHtqgLMJl5AnLlvQ4Wt1Ztch1+BFzAvALVy3Bj39+2fE4M4FI9dkVenKYrfqfQoLeH0LncBV0Zv4HIlrtcMhHAXyVa6b+USIqEtE1zHw2rEEK3vC6xFVtoOoMtvJMRekOMTE32EzmmRu/Pzy0oSU5xTyP6Z9NC51wi3iBAfzERcyB2ucweug0doxNtUzuTmJe8hmpJGGIySKMKJcSAOsuy2v1x1ogovuIaIKIJs6dO6c6ROgAfr+E5ZkK7h+bwqbd32yJUdZtsJmP21vQpdWaS4KYm3gZCQHKevQAtDHsearFqRs57zHuEoaYLDoatsjMTzDzIDMP9vX1uf+BEAm6L6Hb19hM87aKum6Dzf64NXnl3/yPv/E1XsE/KvfIyIFpAM6f2dBACaN3b2zqk7qi18DHFWGkEoaYPMKIcikDWGX5/br6Y0JCUaVoW6shOi25rX5Tp4xCqxFo99lXAvhvhfaZqVQxPlnWulXcygLo3GdCcghD0A8A+CQRfR21zdCL4j9PNm4bqG4hh6bLxilpiVATcrP1mdR4SQa6miterG0JQ0w+roJORE8B+CCAq4noNQAPAjAAgJm/COBZ1EIWX0AtbPG3oxqsEB5OX063Ikumy8bJF7/AaFjysnGWHMwkLqB1Qgdqk3nSLPBO9wFIM16iXO5xeZ4BfCK0EQmxY35Zdj8z3ZLFaLXkvFZjXF4wPIUi5nOEJXkSl0yE2GuumERRCygMkjqupCK1XIQmzM3LHWO1lPteY/EWWdFrNCWSeKnGOD5Zxs8uuYv5siV5fOHujXjkjvfByIfd/VIA9NUWgehqAbVLUseVVCT1vwvRLWHt1pDdqr5ks5xNYf/sX53EW5ebv3QFI4+t6/qwa/9JOHWNs2cxDjz0zUSFCGaJ0bvUiUZA9LWAgpLUcSUVEfSU0a4/0WkJ67Z5qcsMtAs2AY2IGafz5Ynw+kwFu5+ZxsiBaVystJfqv2xJvmViERZxuk/CqAUUBUkdV1IRl0uKMMVYlzDiBaclrBerx0uXIgYaRZycmGcGo1b8aaZNMSdAxNyBFb2G4/NJLXeb1HElFRH0FBGGP9FpCevF6vHapcjr+cJCnDTO3PK+a5p+t3cpmnjlPJb2LMpBr5HDFUYOO8amQul+FRRrFySnWvxCDXG5pIgw/IlOS1i3cEUA2LquOcP3CiOnjEpZXjC08c4Sk955njx6Bn994ixGbl8PAC1uN2s9HqBW78Ws+WJ1ywGdr3Eu8e/eEQs9RegsXj+WsNMS1ktPyMOnFmvwjE+WtSGGRHrryun8gh5V+r0fZipVDO89gZED074nVbN0QLsuPyFaRNBTRBj+RLclrFlMSxc4aF0NOLl6LsxWG+GPAPDo9k2N4lxBW9V1Myt6DTw8tKHpswvSKLq6wIHLE89UqhJCmHDE5ZJw7FEtZvRIGF2FnPASXeDk6jGr/QGtySBBW9V1M5eq841SCub1UzU1iYOoQgglQ9Q/IugJRhViuO9YuSObQl7qfThliqqq/e1+Zrox7olXzuNfLl4KfdxZpVJdwP1jU/jM/u811TPvNXIoaPYxwqRg5HGFkVP2P41i81syRIMhgp5gnKJa2rmpvVg+XjogbV3X19KRyN6mzMqF2Vq1v4lXzrdswtlJSkOJpGFvThGk85DTZ2Sy5YaVePmNSkutFy9FvcKwrKO697OOCHqCiSJLzo/l4+SaGZ8sY9+xcouY37u537EE7+ih046WOWEx4sZujQrh4GWaPH7monYl6CTWQSxr1QQgGaLBcG0SHRXSJNodXRnbUrGAIztvjvWcTucZ3rYW99c3Q/1ibRDdK5mfsVOqNxD3um/j9/7SNTdf2pNTbt62c+9nBacm0RLlkmDCzpIbnyxrLWezd6iZaOIWiuZkQQ0NlJo63lghAE4dzmYqVVyYrWWNuom5kSM8tn2T4zFCe5gx6l5DFf1a1jrXChEkQzQAIugJJswsOdMScsL6pR3ee8JR1N1i4kduX68MfWSgKSOxHaoL3AiLFDqHU6ii31wJndDPzFYlQzQA4kNPOGFlyfntGlRd4EYPSpXP1C0KZmigpHW7VKoL6DVyofjHZds0HnQrPb/dkJzCYyVD1D9ioXcJQTaTzKbQquW2l9WDLiOUECw6I+sESRSKC7PFoB2/q0opvhUusinaJbj1CfWD28aUGbUgSUPZJqwNSkkg8ofTpqi4XLoEL4W3vOJk7YeRvSh1zaPHa5x/ngjzGqMvrBBCca2Eh7hcugRzKayqi23kCHlb6ImRJ22kilNmoF9fvYpi7xI8tn2T1HuJiFKxgNG7NjbcIk610heYta4zaTKRPETQu4ihgRImP/chPLZ9U5OPc/TujfjC3RubHtv+b1dB5dJ182+GYbWZ57BGwyxbkofhFO8otFAsGMpJ8a235wAAR3bejEe3b2ppLdh0jl5D/NwpQnzoQgs6t0mxYGDk9vWOy+OBh76prPfhh2LBwNtzCy2REmZhMvHNu1Mw8njkjg0AgN3PTGs/EyeXClCz+Jb3GrgwW20ca+8DK3QWSSwSfKFzmyxb2uP6JW7XPigYeRBBmWxy+NQ5x9K+Qo08keeytk5iDgALQGMymGduqp0vJA8R9Axiby/mtwFBO3U0LgaotW0KtBniNqOxJs3XF9+tmmVL8jDyixZ3eaaC4adPtL1isiL1z5ONRLlkjDDKjuqSPYq9Brbsec4xvMyppK6VUrGgPY8u5NEU8uFta7FjbEqSimyoIoOiqFgpBbKSi1joGSOMRtKqTTAjT3jz0pxrTQ8v3YjM+OWX9tzSiGO2rii2rutrOYfZMGPLnucASIZo2JhJTV7cWVlbIbW7ok0SIugZI4yyo6psv2VLelBdaJZR1UQxNFDCnTeWtFmP9ugIc0VhnSj2HSvjzhtLjXA5a/1ucyIpGHLrhkXByOMLv7YRL++5BY9aIqCKBQNGnlqOzVJ0i+r+S3OfVPlWZIwwGkkDi71FH61XM9T1obRPFGaddOtmm91HbnevOG2AlooFZfejqDv0dAu6nrIv7bkFI7evx7Ili17ZFb0G7ryxhNFDpzNhzQLhrGiThPjQM4bf4khOeMn6tE8Uqi8Io7akV/ncncr5jk+WxV8bIQRoU/dVn/2bl+Yw9t1XG375TraFi6o8QNYaaYigpwzrjb3c0gzCfpOHcfO7ZX2qJgrdF2GeueXLPz5ZdmyHtmv/SRTrMdBC+PjN+LW73IDOtIVTbfQP7z2B3c9MK+99P3hphp4mRNBThP3GtrpB7NZSlNYLAG1yiVOUS6U6j/vHpjB66DSGt63F6KHTjpubtfcp259+KRh51/ILbr1A/Vz1qK1Z3eRiTvTtrBTCXNEmAfGhpwg3izls35/OSjGjVFRfHi9RLuYX0Et4Y6W6IBugPsgTtWxoP7Z9U0u5B/tehn1z0A9RW7NeJoyg936YTWSSgCcLnYh+FcAfAsgD+BIz77E9/1sARgGYOySPM/OXQhxnV+DmJ/RyY4dpLQWxXoYGSph45TyePHrG8dyV6rxr2rnJFUYeALVMZkt7cnh7TjZHrcwza1doTiLlpaiakSOAmmPbO2HNes1tCHrvZ6nao6ugE1EewJ8A+BUArwH4LhEdYObv2w4dY+ZPRjDGrsBLQpCXGzuItaSbSPz444PUQDdTyd2EZGa2in93w0ocefF80+MLC4yPb+7HX584q43C6TZ0lRHdcBJDAhqfPRDO/owfvJZ+TqvfO0y8WOgfAPACM/8IAIjo6wA+CsAu6EIbOIVPmV8Ytxs7iLXkNpF4sV6C1kA3/fAjB6YdBblg5FrEHKj5Uce+cwZXXqEv/9pNFIw8tq7rc83mVaEzFlRNLDptzdoNi+UFA29dnuv4SiENeBH0EoBXLb+/BuAmxXF3EtG/B/DPAHYw86v2A4joPgD3AUB/f7//0WYYL+FTqhtbF+XiFS8TSZBzuGEt8jR66LRW0I0cObarqy6gq6NgiGoF0fJEeH//cuw7VtZOzk4uPb/utU53GbIbFtLlSE1YUS7PAHiKmd8mov8C4CsAWgJcmfkJAE8AtfK5Ib12JvAaPhW2v88pDtwrfn2XK3oNPHhbrQzvA+POm6NXXtHT1YLthrkFMc+sXMVUqvPYtf97LasgXVSUV/dau/WC2iVLfu8w8SLoZQCrLL9fh8XNTwAAM79h+fVLAD7f/tC6i7jCp3Qbk34aFnvdtDIxGyo8MH7ScfOU0N3Wd1hUqgvKzFr7SsyrSIaxqhOiwUs82HcBvJuI1hDREgAfA3DAegARXWP59XYAPwhviN1BXOFTuigTL9EnJsPb1rbU/HDC/PI/9XyLV64JBiBNiqIlSGRI1rIrs4Srhc7Mc0T0SQCHUAtb/DIzTxPRQwAmmPkAgP9GRLcDmANwHsBvRTjmzBLHMrLksBnmlaGBkuvGph2vySuK5EQhRBi1Spd+fNBZy67MEp4yNpj5WWb+18x8AzP/r/pjn6uLOZh5FzOvZ+aNzLyVmU9FOWghPMLqF+m3scW1xYIvt44QHX4rDCaxx2iWSuC2g6T+dzlh1X7x40c3a5sXjBwqVTHBw6CoCOXzgx8fuJd7ppNRKEnYpE0K0iQ6QoLc1GkNxwoaiw7U/OTiWmmPj2/ux+D1KxvJXdaGzlvX9eHwqXONe0o38RKAl/bc0vZYVPeC2bQ6int5y57nPMfQZwGnJtFioUdEEKsh6ZaG02Rjt9r86HOegKU97hmjgp4nj57B4VPnPFnKbi3+2qXTUTCySbuIVD2KiCCF8+Mqtu/F/+ils4u1OYKfTdXqAkTMQ8D+mYxPljG890TTZza894SyxZ81y7RdP3SnBTaspi5ZQAQ9IoLc1HFYGl5bcPmdbLxUXWyHXiOHFb21lH9zc1U2WZs/k5ED0y01zKsLjK8dPdNo8WeGyN55Ywn7jpVDacXWaYFN4iZtXIigR0SQmzoOS8NNqE3rXed31U02qrj6MOWWQbjlfdegYOQbMfPzzDDy1PU3tfmZ6MJIGcC+Y2UMb1vbaNR9+NS50FaHnRbYrJXAbQfxoUdEkMzPOLJFnVYFQVrQWbHH1esyQ7fcsBLHz1z05XapVOfx1POvtiRABY3ysPLOq5bgxz+/3PZ5omJJnlCdZ+0+hRcDwCrWTlUyg6wOw+ya5ec1u1HA7UiUS4SkIcrFKUIAcK7pEiRy4YHxk/jL5880oloKRg6P3PE+AMD9Y1PeBx4RXmu0x80Kl9Z8pWIBM7OX8dZl985FThNpnJEiaY34ihqnKBcR9C7HKcRsx9iU1gokAPdu7sfDQxtCez2/9dTTIr5xYeQICwDmNTGhbtcvylBDNzod+uiXOCcbJ0FPlbux3WwwySZrxcn/6LR0ZwCHT53z/XpOPnuV79XJ7775F1f4fv1uorrAuGppT2Pz2ArBuV5PsWDEKp5xRXx5wWsgQRykxkJvd8ZO+oyfRLz40EvFgi8rZc3Og1qr3+5GcHMrEJLbQrpULGBuft7RF18w8rjzxhLGvvNqSzSKG7UsW/f2e2aykLWjlJfrFndSju4+CSv5qR3iTmTKhIXe7oyd5Bk/qZjWuy4c0Ezh92OlLC/ouwvZxfvNS3NYtkQf+phUMTdyhJnZy45ibjZzfnhoA0bv3ugr5JLgvHKxYq6yzByBUrHg6br5cX1FQZJjy5OcyJQaQW/3Iib5Q4gTNzfU0EAJX/i1jUpXiF0YvEyQfkLFqwuMWZdNvSRSXWDXzch5ZoweOo3xyTKGBkq456ZVnkWaAccuTiaqCCk/93ucbskkx5YnebJJTdhiuyU7peRnK15LDajC0PyEuVk3kPxa1Um1wsOgPFPBjrEp7J04g+NnLobyXnP1lnQ6F5ifImpxlp6II/TRK3E1o/GC+NC72Ifeji/Q6W/NeiGvz1RQ7DXw5qU53z5iE4lk8UcOwB9s3+S5XpAX4vanJ5GkRrmkRtCB9i+ixLU247RBSdBbeYB+gjRTyMOozWLkCNs/sMrxfEneGI0LNwEenyzjU9844XmiTMJGpLBIZqottpsNJtlkzTgtv60bnUDrklu3JFZtPrtRLBi4deM1OPi9s42N0WLBwMjttUbSg9evVHZEchPzLTesVDZOjoNiwQBRZ3qkuvnJzc/Oq6WeVLekGGitpMpCF8LF6/Lbz5Lbyeq348fy07l4dC4ZQi2ixk9bvDAwJxmq+7KBxckJgDZZK8yVhtfPyxrKaF5H+ziS6pbsZhdqZix0IVy81jD3ExnhZ9PNtPzslpa9IcPwtrXaMahECPXf4xJzYFHMUR/Hrv0n8cgdG3Dv5n587eiZFtG888YS/vrE2bbHbOTI8+acylJny/soJdjq7XTN9bSQmrBFIRq81DD3s+RWhZvlFPF4ZlTAA+MnsWNsqime/cmjZ1ri24uKbEcAnuOqoyRH7ha2KTYPD23Ao9s3tWTmPjy0AcuWtmdfFQsGRu/e6EvQVMJoivmRnTcnVhwlDFmNWOhCA7/hWE4+TPvjusfs1qqK2ni4pZCUOTbdBl8uQCekHIB8vZqhV7wG8Jhio9vLCSJGeSIsMAf2IQcRxiT4riUMWY0IutDA/FLufma6sXm3tEe9iHOLYVd9we2PbdnznGfrulJdwMc397e4YoYGStoqjQvsrxNSngj33LQKg9evbLoGYeEmNm7uKtUqwJzIgsaM614zR4Q1Ow+2CHZS2iQmORY8TsTlIrTw5qW5xs8zlSqG955ou4ORCr8W6eFT5xruIas7QFV8KgjzzNh3rPY+e5eEa+t4ERu3501XCEHdnSlIKQtdZ6l5ZmVJhySV0LjCWJSvuIuJJQURdKEJXduykQPTTY95Xao/MH4SN+x6Fqt3HsQNu57FA+MnG8/5XR7rslAvhmhJm+LUri+WqDbR+OmgMzRQQtGh1o3p135pzy1Y0ESn+R23vdqm20SRBN+1uUqwrqDennMvhdANiKALTeiiLOyPe6lnYXYosraIe/LomYao66zDHtUuquY1Rw5MI+yvsunSCUrByOPem/obVv5bb89h9zPTnso2j9y+Hobi/Rv55uiVMOuJWDfG3SaKJNQxSdIqIWmIoAuB8FI86annX1X+rfn40EAJd95YailKRagJmNO5TaIITTT9xkGaXBOA9/cvb2q4PFOp4sJs1VNVyqGBEkbv3thkqa/oNTB6V3P0SlTFq9wEOwlFs5KwSkgqsikqNKGrQW73U3spnqRLLbc+fvjUuZaNvuoCo1gwsGxpT8cjKUxxsr4/P6VkGcDRH11wTKt3i5f2ktEcVfEqt83GJBTNkggXPSLoQhMP3rYew0+faArbM/KEB29b33Ksm/DosjitflqdVXWxUsXUgx9yHa9bEwzz9e65aRVeOvemYykAa7kBYPH9+S1o5aVGShj1xqMoZeFFsOMuoRFGhEsSQi+jQARdaCJMC+yem1bhyaNnlI+btGttqSYgK/Y0+Hv/9//TivqypT2O4Zb2NHmnCctN1Alo1EJPGnELthvt3qNJCb2MAhF0oYWwvtBmA+mnnn+1IYD33LSqqbF0u9aWOU5V8S7VeV5+Q28ZO1nNqmvSTsVJBro+Tb0d2rlHs1w2QARdiJSHhzY0CbidMFYEVteI23mcNs78Ws1OYx+8fmWoNXKE8MjypqpUWxS6Cl3VRpMomjnE3VRYaCbtn0fbTaKJ6FeJ6DQRvUBEOxXPLyWisfrzzxPR6jbHLAiR4BaOGIWVloRQP2GRLH8eroJORHkAfwLgwwDeA+AeInqP7bD/BOACM/8rAI8C+P2wByoIYWBmRqoyIoFoQt/s2ZheM0eFaMjy5+HFh/4BAC8w848AgIi+DuCjAL5vOeajAEbqPz8N4HEiIo7LnyMIDug69kRppSU9cqTbyOrn4cXlUgJgTfl7rf6Y8hhmngNwEcA77CciovuIaIKIJs6dOxdsxIIQAlm20oTupaNRLsz8BIAngNqmaCdfWxDsZNVKE7oXLxZ6GcAqy+/X1R9THkNEPQCWA3gjjAEKgiAI3vAi6N8F8G4iWkNESwB8DMAB2zEHAPxm/ee7ADwn/nNBEITO4upyYeY5IvokgEMA8gC+zMzTRPQQgAlmPgDgzwH8HyJ6AcB51ERfEARB6CCefOjM/CyAZ22Pfc7y8yUAd4c7NEEQBMEPUg9dEAQhI8SW+k9E5wC8EsuLB+NqAD+NexAxI9dArgEg1wCI9xpcz8x9qidiE/S0QUQTuvoJ3YJcA7kGgFwDILnXQFwugiAIGUEEXRAEISOIoHvnibgHkADkGsg1AOQaAAm9BuJDFwRByAhioQuCIGQEEXRBEISMIIJugYhWEtG3iOiH9f9XaI77zfoxPySi31Q8f4CI/in6EYdPO9eAiHqJ6CARnSKiaSLa09nRB6edrlxEtKv++Gki2tbRgYdI0GtARL9CRMeI6GT9/+T3cdPQbnc2IuonojeJ6NMdG7QVZpZ/9X8APg9gZ/3nnQB+X3HMSgA/qv+/ov7zCsvzdwD4SwD/FPf76fQ1ANALYGv9mCUAvg3gw3G/Jw/vOQ/gRQC/WB/3CQDvsR3zXwF8sf7zxwCM1X9+T/34pQDW1M+Tj/s9dfgaDAC4tv7zewGU434/nb4GluefBrAXwKfjeA9ioTfzUQBfqf/8FQBDimO2AfgWM59n5gsAvgXgVwGAiK4E8HsAHo5+qJER+Bow8ywzHwYAZr4M4Dhq5ZaTTqMrV33cZlcuK9br8jSAXyYiqj/+dWZ+m5lfAvBC/XxpI/A1YOZJZn69/vg0gAIRLe3IqMOlnfsARDQE4CXUrkEsiKA3805mPlv/+V8AvFNxjFMHp/8J4AsAZiMbYfS0ew0AAERUBHAbgL+LYIxh005XLi9/mwbC6kx2J4DjzPx2ROOMksDXoG7M/XcAuzswTi0d7ViUBIjobwG8S/HUZ62/MDMTkeeYTiLaBOAGZt5h96sljaiugeX8PQCeAvBHXO9FK2QfIlqPWoP4D8U9lhgYAfAoM79JmgbknaDrBJ2Z/4PuOSL6MRFdw8xniegaAD9RHFYG8EHL79cB+HsAvwRgkIheRu26/gIR/T0zfxAJI8JrYPIEgB8y82Ptj7Yj+OnK9ZqtK5eXv00D7VwDENF1AP4KwG8w84vRDzcS2rkGNwG4i4g+D6AIYIGILjHz45GP2krcGxFJ+gdgFM0bgp9XHLMSNT/Zivq/lwCstB2zGundFG3rGqC2f7APQC7u9+LjPfegtrG7BoubYettx3wCzZth36j/vB7Nm6I/Qjo3Rdu5BsX68XfE/T7iuga2Y0YQ06Zo7BcxSf9Q8wf+HYAfAvhbi0gNAviS5bj/iNrm1wsAfltxnjQLeuBrgJpFwwB+AGCq/u8/x/2ePL7vjwD4Z9SiHD5bf+whALfXf74CteiFFwB8B8AvWv72s/W/O40URPWEfQ0APADgLctnPgXgF+J+P52+DyzniE3QJfVfEAQhI0iUiyAIQkYQQRcEQcgIIuiCIAgZQQRdEAQhI4igC4IgZAQRdEEQhIwggi4IgpAR/j/mvexWPSE/DgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(model.fittedvalues, model.resid_pearson ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "configured-poison",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
